Neural structure mapping in human probabilistic reward learning
Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented by a single dimension (i.e. on a mental line), for example according to their cardinality, size or value. Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their pay-out probability in a way that suggested they were encoded onto the same mental number line. Our findings suggest that in humans, learning about values is accompanied by structural alignment of value representations with neural codes for the concept of magnitude.